Image restoration tasks have witnessed great performance improvement in
recent years by developing large deep models. Despite the outstanding
performance, the heavy computation demanded by the deep models has restricted
the application of image restoration. To lift the restriction, it is required
to reduce the size of the networks while maintaining accuracy. Recently, N:M
structured pruning has appeared as one of the effective and practical pruning
approaches for making the model efficient with the accuracy constraint.
However, it fails to account for different computational complexities and
performance requirements for different layers of an image restoration network.
To further optimize the trade-off between the efficiency and the restoration
accuracy, we propose a novel pruning method that determines the pruning ratio
for N:M structured sparsity at each layer. Extensive experimental results on
super-resolution and deblurring tasks demonstrate the efficacy of our method
which outperforms previous pruning methods significantly. PyTorch
implementation for the proposed methods will be publicly available at
this https URL

本文提出了一种新的修剪方法，用于在不影响恢复精度的情况下减小图像修复网络的尺寸，该方法根据每个层的不同计算复杂度和性能要求确定 N:M 结构稀疏的修剪比，通过大量的实验结果表明，该方法在超分辨率和去模糊任务中的表现优于以前的修剪方法。